In this article, I intend to discuss the importance of market data and econometrics for decentralized financing (DeFi) and DeFi-applied research for crypto (and digital) assets as a consequence of financial econometrics and applied research. I will also try to draw on the views and insights of Eugene Fama’s masterpieces, based on his interest in measuring the statistical properties of stock prices and resolving disputes between technical analysis (using geometric patterns in price and volume charts to predict future prices movements). collateral) and basic analysis (use of accounting and financial data to determine the fair value of financing securities). Nobel laureate Fama realized the effective market hypothesis, which is summarized in an article that “prices fully reflect all available information” in efficient markets.

So let’s focus on this information about cryptocurrencies and digital assets, cryptocurrencies and decentralized financial data sources, market data analysis and everything related to the huge emerging DeFi industry, which is important in attracting institutional investors to cryptocurrencies, as well as on DeFi and “token”. ┬╗Markets in general. …

In most markets, market data is defined as the price of an instrument (assets, shares, commodities, etc.) and transaction data. These data reflect market volatility, asset class, volume and trade-specific data such as open, high, low, close, volume (OHLCV) and other value-added data such as order box data (supply and demand, total market depth, etc.), and pricing and valuation (benchmark data, traditional economic data such as first exchange rates, etc.). This market data is useful in various financial econometrics, applied economics, and now in DeFi research, for example:

Framework for risk management and risk models
quantitative trade
Price and rating
Portfolio storage and management
End-to-end cryptocurrency financing
Although the application of traditional methods to assess risk and characterize the varying degree of distribution of opportunities across different and new classes of cryptoassets may be limiting, this is only the beginning. New valuation models have emerged that seek to understand these digital assets that have become truly dominant in global digital markets, and even these models require market data. Some of these models include, but are not limited to:

VWAP, or volume-weighted average price, is a methodology that usually determines the fair value of a digital asset by calculating a weighted average price based on a predefined set of after-sales data available on component exchanges.
TWAP, or time-weighted average price, which can be an Oracle contract or a smart contract that draws token prices from liquidity pools using a period to determine the degree of security.
The growth rate determines the safety factor.
TVL, or Total Locked Value, is for liquidity aggregators and automated marketers (AMM).
The total number of users reflects the impact of the network, its potential use and growth.
The primary market method is used in the primary market, which is often defined as the market with the highest volume and activity for a digital asset. Fair value will be the price received for a digital asset in that market.
The volumes of CEX and DEX consist of the volumes of centralized exchanges (CEX) and decentralized exchanges (DEX).
CVI, or Cryptocurrency Volatility Index, is created by calculating a decentralized volatility index based on cryptocurrency price prices together with an analysis of the market’s expectations for future volatility.
In this way, market data becomes central in all modeling and analysis tools to understand the markets, as well as to perform correlation analysis between different crypto segments such as Layer 1, Layer 2, Web 3.0 and DeFi. The most important data source for this cryptocurrency market is the growing and fragmented mix of cryptocurrency exchanges. Data from these exchanges can not be trusted by the general public, as we have seen cases of volume spikes due to practices such as money laundering and closed pools that can distort the price by distorting demand and volume. Consequently, it can be challenging to model a hypothesis based on empirical data and then test the hypothesis to formulate investment theory (conclusions from empirical summaries). This leads to the emergence of an oracle that seeks to solve problems with reliable data contained in the blockchain transaction system or the intermediate level between the levels of cryptography and traditional economics.

Source: CoinTelegraph